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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
PD Controller: Design01:26

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
PI Controller: Design01:24

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
Controller Configurations01:22

Controller Configurations

Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...

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Related Experiment Video

Updated: May 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Neuro-Adaptive Integral Sliding-Mode Consensus Control for Multi-ASVs With Unknown Nonlinearity via Dissipative

Sayekat Kumar Das, Pratap Anbalagan, Yibin Tian

    IEEE Transactions on Cybernetics
    |May 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel control strategy for multiple autonomous surface vehicles (ASVs) facing unknown disturbances. The new method enhances stability and performance by adaptively estimating and compensating for external factors.

    Related Experiment Videos

    Last Updated: May 16, 2026

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    Area of Science:

    • Robotics and Control Systems
    • Marine Engineering
    • Artificial Intelligence

    Background:

    • Autonomous surface vehicles (ASVs) require robust control strategies to navigate complex marine environments.
    • Unknown nonlinearities and external disturbances pose significant challenges to ASV control system stability and performance.
    • Existing control methods often struggle to effectively address both system uncertainties and unpredictable environmental factors simultaneously.

    Purpose of the Study:

    • To develop an advanced control system for multiple ASVs that can handle unknown nonlinearities and external disturbances.
    • To enhance the robustness and adaptive capabilities of integral sliding-mode control (ISMC) for ASV applications.
    • To ensure precise trajectory tracking and formation control for multi-ASV systems under uncertain conditions.

    Main Methods:

    • A dissipative disturbance-estimator (DDE)-based neuro-adaptive integral sliding-mode control (ISMC) framework was designed.
    • Radial basis function neural networks (RBFNNs) and state-dependent input matrices were integrated into a memory-based integral sliding manifold.
    • A disturbance observer (DOB) was developed for estimating and compensating for mismatched external disturbances.
    • Adaptive dynamic laws were formulated to estimate neural network approximation errors.
    • An asymmetric Lyapunov-Krasovskii functional (ALKF) and improved integral inequality were utilized to derive dissipative performance criteria via linear matrix inequalities (LMIs).

    Main Results:

    • The proposed neuro-adaptive ISMC effectively estimated and compensated for unknown nonlinearities and external disturbances in multi-ASV systems.
    • The disturbance observer successfully identified and mitigated the impact of mismatched disturbances.
    • The adaptive control laws ensured stability by estimating upper bounds of approximation errors.
    • Dissipative performance criteria derived from LMIs yielded effective disturbance-rejection gains.
    • Numerical simulations demonstrated the superior performance and effectiveness of the proposed control strategy compared to existing methods.

    Conclusions:

    • The developed DDE-based neuro-adaptive ISMC provides a highly effective and robust solution for controlling multiple ASVs in challenging environments.
    • The integration of RBFNNs, DOB, and ALKF significantly improves disturbance rejection and system stability.
    • The proposed method offers a promising advancement for cooperative control of autonomous marine systems.